{"cells":[{"cell_type":"markdown","metadata":{"id":"81A4F02267A44FFCB7802E0E8583AB69","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","runtime":{"execution_status":null,"is_visible":false,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":["# 数据读取"]},{"cell_type":"code","execution_count":3,"metadata":{"collapsed":false,"id":"74D7B0518F1B4D98AB4DBA52EE2A125B","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["# 导入需要的库\n","import pandas as pd\n","import numpy as np\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","from sklearn.preprocessing import OneHotEncoder\n","from sklearn.model_selection import train_test_split,GridSearchCV\n","from sklearn.utils import resample\n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.metrics import classification_report,confusion_matrix,roc_curve, auc"]},{"cell_type":"code","execution_count":4,"metadata":{"collapsed":false,"id":"D36E3C3E27C54D3981AAC80BC9B6DD32","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["# 读取数据\n","data = pd.read_csv(\"D:/HR_Analytics.csv\")"]},{"cell_type":"markdown","metadata":{"id":"A0CAB0A01FE842E69EAE3626EB1906E2","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","runtime":{"execution_status":null,"is_visible":false,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":["# 数据处理"]},{"cell_type":"markdown","metadata":{"id":"1F30EA961BBD45628A4BA9731A7C3E29","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","runtime":{"execution_status":null,"is_visible":false,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":["## 数据预览"]},{"cell_type":"code","execution_count":5,"metadata":{"collapsed":false,"id":"A2ADB587154347BDB50A507A53F7D7D0","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/plain":["(1480, 38)"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["# 查看数据维度\n","data.shape"]},{"cell_type":"code","execution_count":6,"metadata":{"collapsed":false,"id":"E67207B728704FA99282ABA91262DFAB","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 1480 entries, 0 to 1479\n","Data columns (total 38 columns):\n"," #   Column                    Non-Null Count  Dtype  \n","---  ------                    --------------  -----  \n"," 0   EmpID                     1480 non-null   object \n"," 1   Age                       1480 non-null   int64  \n"," 2   AgeGroup                  1480 non-null   object \n"," 3   Attrition                 1480 non-null   object \n"," 4   BusinessTravel            1480 non-null   object \n"," 5   DailyRate                 1480 non-null   int64  \n"," 6   Department                1480 non-null   object \n"," 7   DistanceFromHome          1480 non-null   int64  \n"," 8   Education                 1480 non-null   int64  \n"," 9   EducationField            1480 non-null   object \n"," 10  EmployeeCount             1480 non-null   int64  \n"," 11  EmployeeNumber            1480 non-null   int64  \n"," 12  EnvironmentSatisfaction   1480 non-null   int64  \n"," 13  Gender                    1480 non-null   object \n"," 14  HourlyRate                1480 non-null   int64  \n"," 15  JobInvolvement            1480 non-null   int64  \n"," 16  JobLevel                  1480 non-null   int64  \n"," 17  JobRole                   1480 non-null   object \n"," 18  JobSatisfaction           1480 non-null   int64  \n"," 19  MaritalStatus             1480 non-null   object \n"," 20  MonthlyIncome             1480 non-null   int64  \n"," 21  SalarySlab                1480 non-null   object \n"," 22  MonthlyRate               1480 non-null   int64  \n"," 23  NumCompaniesWorked        1480 non-null   int64  \n"," 24  Over18                    1480 non-null   object \n"," 25  OverTime                  1480 non-null   object \n"," 26  PercentSalaryHike         1480 non-null   int64  \n"," 27  PerformanceRating         1480 non-null   int64  \n"," 28  RelationshipSatisfaction  1480 non-null   int64  \n"," 29  StandardHours             1480 non-null   int64  \n"," 30  StockOptionLevel          1480 non-null   int64  \n"," 31  TotalWorkingYears         1480 non-null   int64  \n"," 32  TrainingTimesLastYear     1480 non-null   int64  \n"," 33  WorkLifeBalance           1480 non-null   int64  \n"," 34  YearsAtCompany            1480 non-null   int64  \n"," 35  YearsInCurrentRole        1480 non-null   int64  \n"," 36  YearsSinceLastPromotion   1480 non-null   int64  \n"," 37  YearsWithCurrManager      1423 non-null   float64\n","dtypes: float64(1), int64(25), object(12)\n","memory usage: 439.5+ KB\n"]}],"source":["# 查看数据信息\n","data.info()"]},{"cell_type":"code","execution_count":7,"metadata":{"collapsed":false,"id":"FC2AD7E042C0492588E3F3F78780AADC","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/plain":["EmpID                        0\n","Age                          0\n","AgeGroup                     0\n","Attrition                    0\n","BusinessTravel               0\n","DailyRate                    0\n","Department                   0\n","DistanceFromHome             0\n","Education                    0\n","EducationField               0\n","EmployeeCount                0\n","EmployeeNumber               0\n","EnvironmentSatisfaction      0\n","Gender                       0\n","HourlyRate                   0\n","JobInvolvement               0\n","JobLevel                     0\n","JobRole                      0\n","JobSatisfaction              0\n","MaritalStatus                0\n","MonthlyIncome                0\n","SalarySlab                   0\n","MonthlyRate                  0\n","NumCompaniesWorked           0\n","Over18                       0\n","OverTime                     0\n","PercentSalaryHike            0\n","PerformanceRating            0\n","RelationshipSatisfaction     0\n","StandardHours                0\n","StockOptionLevel             0\n","TotalWorkingYears            0\n","TrainingTimesLastYear        0\n","WorkLifeBalance              0\n","YearsAtCompany               0\n","YearsInCurrentRole           0\n","YearsSinceLastPromotion      0\n","YearsWithCurrManager        57\n","dtype: int64"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["# 查看各列缺失值\n","data.isna().sum()"]},{"cell_type":"code","execution_count":8,"metadata":{"collapsed":false,"id":"86917D388DE649B9BCF528AD2B72A71B","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/plain":["7"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["# 查看重复值\n","data.duplicated().sum()"]},{"cell_type":"markdown","metadata":{"id":"FB91385B88904697811D4BE3FA399F43","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","runtime":{"execution_status":null,"is_visible":false,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":["## 数据处理"]},{"cell_type":"code","execution_count":9,"metadata":{"collapsed":false,"id":"731155ED823F4C998403DC72088C1F24","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["# 删除重复值\n","data.drop_duplicates(inplace=True)"]},{"cell_type":"code","execution_count":10,"metadata":{"collapsed":false,"id":"ADD7D08CB0B2437DBF7267752C9AEDBA","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["# 删除缺失值\n","data.dropna(subset=['YearsWithCurrManager'],inplace=True)"]},{"cell_type":"code","execution_count":11,"metadata":{"collapsed":false,"id":"725B888867BC40C49391EFC2C0BB2DE8","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/plain":["EmpID             1413\n","AgeGroup             5\n","Attrition            2\n","BusinessTravel       4\n","Department           3\n","EducationField       6\n","Gender               2\n","JobRole              9\n","MaritalStatus        3\n","SalarySlab           4\n","Over18               1\n","OverTime             2\n","dtype: int64"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["# 查看每个对象类型字段的唯一值数量\n","data.select_dtypes(include=['object']).nunique()"]},{"cell_type":"code","execution_count":12,"metadata":{"collapsed":false,"id":"370784FB13BB45A6B2C644303757D188","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/plain":["1"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["# 检查StandardHours列是否为常数\n","data['StandardHours'].nunique()"]},{"cell_type":"code","execution_count":13,"metadata":{"collapsed":false,"id":"1AEB25BDC30446CA9E6B709D88F71DBB","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["# 删除 'StandardHours' 字段\n","data.drop('StandardHours', axis=1,inplace=True)"]},{"cell_type":"code","execution_count":14,"metadata":{"collapsed":false,"id":"B2C7795DAE9E4024B8C0648948849341","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["# 转换字段为'category'数据类型\n","category_columns = ['AgeGroup', 'Attrition', 'BusinessTravel', 'Department',\n","                    'EducationField', 'Gender', 'JobRole', 'MaritalStatus', 'SalarySlab']\n","\n","data[category_columns] = data[category_columns].astype('category')\n","# 转换EmpID为字符串类型\n","data['EmpID'] = data['EmpID'].astype(str)"]},{"cell_type":"code","execution_count":15,"metadata":{"collapsed":false,"id":"5B8D11B832E945D2948E2E6573248387","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"text/plain":["EmpID                         object\n","Age                            int64\n","AgeGroup                    category\n","Attrition                   category\n","BusinessTravel              category\n","DailyRate                      int64\n","Department                  category\n","DistanceFromHome               int64\n","Education                      int64\n","EducationField              category\n","EmployeeCount                  int64\n","EmployeeNumber                 int64\n","EnvironmentSatisfaction        int64\n","Gender                      category\n","HourlyRate                     int64\n","JobInvolvement                 int64\n","JobLevel                       int64\n","JobRole                     category\n","JobSatisfaction                int64\n","MaritalStatus               category\n","MonthlyIncome                  int64\n","SalarySlab                  category\n","MonthlyRate                    int64\n","NumCompaniesWorked             int64\n","Over18                        object\n","OverTime                      object\n","PercentSalaryHike              int64\n","PerformanceRating              int64\n","RelationshipSatisfaction       int64\n","StockOptionLevel               int64\n","TotalWorkingYears              int64\n","TrainingTimesLastYear          int64\n","WorkLifeBalance                int64\n","YearsAtCompany                 int64\n","YearsInCurrentRole             int64\n","YearsSinceLastPromotion        int64\n","YearsWithCurrManager         float64\n","dtype: object"]},"execution_count":15,"metadata":{},"output_type":"execute_result"}],"source":["# 确认转换后的数据类型\n","data.dtypes"]},{"cell_type":"code","execution_count":16,"metadata":{"collapsed":false,"id":"C8CCBBD2DC294E15BD5AD08653FF0D8D","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[],"source":["data['BusinessTravel'] = data['BusinessTravel'].replace('TravelRarely', 'Travel_Rarely') # 这是后面分析出差的时候发现需要替换一下数值"]},{"cell_type":"markdown","metadata":{"id":"D99FFB80A5B54E478450DB1FDD3A8200","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","runtime":{"execution_status":null,"is_visible":false,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":["# 离职情况分析"]},{"cell_type":"markdown","metadata":{},"source":["## 性别与离职率关系"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 2000x1500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["\n","import pandas as pd\n","# 性别与离职率的关系\n","gender_attrition = pd.crosstab(data['Gender'], data['Attrition'])\n","gender_attrition_percent = gender_attrition.div(gender_attrition.sum(axis=1), axis=0) * 100\n","\n","fig = plt.figure(figsize=(20,15))\n","\n","# 添加前两个小图\n","ax1 = fig.add_subplot(2, 2, 1)\n","\n","# 性别与离职率的关系\n","gender_attrition_percent['Yes'].plot(kind='bar', ax=ax1)\n","ax1.set_title('Attrition Rate by Gender')\n","ax1.set_xlabel('Gender')\n","ax1.set_ylabel('Attrition Rate (%)')\n","ax1.set_xticklabels(ax1.get_xticklabels(), rotation=0)\n","# 添加数据标签\n","for p in ax1.patches:\n","    ax1.annotate(f\"{p.get_height():.1f}%\", (p.get_x() + p.get_width() / 2., p.get_height()),\n","                       ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n","\n","\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"CA97AD4BE3724556B547F91910CABF66","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","runtime":{"execution_status":null,"is_visible":false,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":["## 教育与离职率关系"]},{"cell_type":"code","execution_count":18,"metadata":{"collapsed":false,"id":"F916969B4E684C2B9C83A67362320356","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"image/png":"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size 2000x600 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# 分析教育等级与离职率的关系\n","education_level_attrition = pd.crosstab(data['Education'], data['Attrition'])\n","education_level_attrition_percent = education_level_attrition.div(education_level_attrition.sum(axis=1), axis=0) * 100\n","\n","# 分析教育背景与离职率的关系\n","education_field_attrition = pd.crosstab(data['EducationField'], data['Attrition'])\n","education_field_attrition_percent = education_field_attrition.div(education_field_attrition.sum(axis=1), axis=0) * 100\n","\n","plt.figure(figsize=(20, 6))\n","\n","# 教育等级与离职率的关系\n","ax1 = plt.subplot(1, 2, 1)\n","education_level_attrition_percent['Yes'].plot(kind='bar', ax=ax1)\n","plt.title('Attrition Rate by Education Level')  # 按教育等级划分的离职率\n","plt.xlabel('Education Level')  # 教育等级\n","plt.ylabel('Attrition Rate (%)')  # 离职率（百分比）\n","plt.xticks(rotation=0)\n","# 添加数据标签\n","for p in ax1.patches:\n","    ax1.annotate(f\"{p.get_height():.1f}%\", (p.get_x() + p.get_width() / 2., p.get_height()),\n","                ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n","\n","# 教育背景与离职率的关系\n","ax2 = plt.subplot(1, 2, 2)\n","education_field_attrition_percent['Yes'].plot(kind='bar', ax=ax2)\n","plt.title('Attrition Rate by Education Field')  # 按教育领域划分的离职率\n","plt.xlabel('Education Field')  # 教育领域\n","plt.ylabel('Attrition Rate (%)')  # 离职率（百分比）\n","plt.xticks(rotation=0)\n","# 添加数据标签\n","for p in ax2.patches:\n","    ax2.annotate(f\"{p.get_height():.1f}%\", (p.get_x() + p.get_width() / 2., p.get_height()),\n","                ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n","\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"7D1BFF9D18994D98BF754CA782F0D40A","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","runtime":{"execution_status":null,"is_visible":false,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":["## 工作与离职率关系"]},{"cell_type":"code","execution_count":19,"metadata":{"collapsed":false,"id":"50BD41E4A61A41C9BEA99CB037702250","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"image/png":"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size 2000x1500 with 4 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# 出差\n","business_travel_attrition = pd.crosstab(data['BusinessTravel'], data['Attrition'])\n","business_travel_attrition_percent = business_travel_attrition.div(business_travel_attrition.sum(axis=1), axis=0) * 100\n","\n","# 部门\n","department_attrition = pd.crosstab(data['Department'], data['Attrition'])\n","department_attrition_percent = department_attrition.div(department_attrition.sum(axis=1), axis=0) * 100\n","\n","plt.figure(figsize=(20, 15))\n","# 绘制出差离职率\n","plt.subplot(2, 3, 1)\n","business_travel_attrition_percent['Yes'].plot(kind='bar', color='skyblue')\n","plt.title('Attrition Rate by Business Travel')\n","plt.xlabel('Business Travel')\n","plt.ylabel('Attrition Rate (%)')\n","for p in plt.gca().patches:\n","    plt.gca().annotate(f\"{p.get_height():.1f}%\", (p.get_x() + p.get_width() / 2., p.get_height()),\n","                       ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n","plt.xticks(rotation=0)\n","# 绘制各部门的离职率图表\n","plt.subplot(2, 3, 2)\n","department_attrition_percent['Yes'].plot(kind='bar', color='lightgreen')\n","plt.title('Attrition Rate by Department')\n","plt.xlabel('Department')\n","plt.ylabel('Attrition Rate (%)')\n","for p in plt.gca().patches:\n","    plt.gca().annotate(f\"{p.get_height():.1f}%\", (p.get_x() + p.get_width() / 2., p.get_height()),\n","                       ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n","plt.xticks(rotation=0)\n","# 通勤距离与离职的关系\n","plt.subplot(2, 3, 3)\n","sns.boxplot(x='Attrition', y='DistanceFromHome', data=data)\n","plt.title('Attrition by Distance From Home')  # 按通勤距离划分的离职情况\n","plt.xlabel('Attrition Status')  # 离职状态\n","plt.ylabel('Distance From Home')  # 通勤距离\n","plt.xticks(rotation=0)\n","# 工作角色与离职率的关系\n","job_role_attrition_percent = pd.crosstab(data['JobRole'], data['Attrition'], normalize='index') * 100\n","plt.subplot(2, 3, (4, 6))  # 横跨第二行的三列\n","job_role_attrition_percent['Yes'].plot(kind='bar', color='lightblue')\n","plt.title('Attrition Rate by Job Role')\n","plt.xlabel('Job Role')\n","plt.ylabel('Attrition Rate (%)')\n","for p in plt.gca().patches:\n","    plt.gca().annotate(f\"{p.get_height():.1f}%\", (p.get_x() + p.get_width() / 2., p.get_height()),\n","                       ha='center', va='center', xytext=(0, 10), textcoords='offset points')\n","plt.xticks(rotation=0)\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"B9474F1BD2B64C8C8361C83AB4CFC45F","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","runtime":{"execution_status":null,"is_visible":false,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":["## 评分与离职率关系"]},{"cell_type":"code","execution_count":20,"metadata":{"collapsed":false,"id":"1BAA274533674E4FAFAB79049867F474","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 2000x1600 with 4 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(20, 16))\n","\n","# 工作环境满意度与离职的关系\n","env_sat_attrition_counts = pd.crosstab(data['EnvironmentSatisfaction'], data['Attrition'])\n","env_sat_attrition_percent = env_sat_attrition_counts.div(env_sat_attrition_counts.sum(1), axis=0) * 100\n","plt.subplot(2, 2, 1)\n","ax_env = env_sat_attrition_counts.plot(kind='bar', stacked=True, ax=plt.gca())\n","plt.title('Attrition by Environment Satisfaction (Stacked)')\n","plt.xlabel('Environment Satisfaction')\n","plt.ylabel('Number of Employees')\n","plt.xticks(rotation=0)\n","for i, rect in enumerate(ax_env.patches):\n","    if i >= len(env_sat_attrition_counts):\n","        height = rect.get_height()\n","        if height > 0:\n","            percentage = env_sat_attrition_percent['Yes'].iloc[i % len(env_sat_attrition_counts)]\n","            x = rect.get_x() + rect.get_width() / 2\n","            y = rect.get_y() + height / 2\n","            ax_env.text(x, y, f'{percentage:.1f}%', ha='center', va='center', color='white')\n","\n","# 工作满意度与离职的关系\n","job_sat_attrition_counts = pd.crosstab(data['JobSatisfaction'], data['Attrition'])\n","job_sat_attrition_percent = job_sat_attrition_counts.div(job_sat_attrition_counts.sum(1), axis=0) * 100\n","plt.subplot(2, 2, 2)\n","ax_job = job_sat_attrition_counts.plot(kind='bar', stacked=True, ax=plt.gca())\n","plt.title('Attrition by Job Satisfaction (Stacked)')\n","plt.xlabel('Job Satisfaction')\n","plt.ylabel('Number of Employees')\n","plt.xticks(rotation=0)\n","for i, rect in enumerate(ax_job.patches):\n","    if i >= len(job_sat_attrition_counts):\n","        height = rect.get_height()\n","        if height > 0:\n","            percentage = job_sat_attrition_percent['Yes'].iloc[i % len(job_sat_attrition_counts)]\n","            x = rect.get_x() + rect.get_width() / 2\n","            y = rect.get_y() + height / 2\n","            ax_job.text(x, y, f'{percentage:.1f}%', ha='center', va='center', color='white')\n","\n","# 关系满意度与离职的关系\n","relationship_satisfaction_attrition_counts = pd.crosstab(data['RelationshipSatisfaction'], data['Attrition'])\n","relationship_satisfaction_attrition_percent = relationship_satisfaction_attrition_counts.div(relationship_satisfaction_attrition_counts.sum(1), axis=0) * 100\n","plt.subplot(2, 2, 3)\n","ax_wlb = relationship_satisfaction_attrition_counts.plot(kind='bar', stacked=True, ax=plt.gca())\n","plt.title('Attrition by Relationship Satisfaction (Stacked)')\n","plt.xlabel('Relationship Satisfaction')\n","plt.ylabel('Number of Employees')\n","plt.xticks(rotation=0)\n","for i, rect in enumerate(ax_wlb.patches):\n","    if i >= len(relationship_satisfaction_attrition_counts):\n","        height = rect.get_height()\n","        if height > 0:\n","            percentage = relationship_satisfaction_attrition_percent['Yes'].iloc[i % len(relationship_satisfaction_attrition_counts)]\n","            x = rect.get_x() + rect.get_width() / 2\n","            y = rect.get_y() + height / 2\n","            ax_wlb.text(x, y, f'{percentage:.1f}%', ha='center', va='center', color='white')\n","\n","# 工作生活平衡评价与离职的关系\n","work_life_balance_attrition_counts = pd.crosstab(data['WorkLifeBalance'], data['Attrition'])\n","work_life_balance_attrition_percent = work_life_balance_attrition_counts.div(work_life_balance_attrition_counts.sum(1), axis=0) * 100\n","plt.subplot(2, 2, 4)\n","ax_wlb = work_life_balance_attrition_counts.plot(kind='bar', stacked=True, ax=plt.gca())\n","plt.title('Attrition by Work-Life Balance (Stacked)')\n","plt.xlabel('Work-Life Balance')\n","plt.ylabel('Number of Employees')\n","plt.xticks(rotation=0)\n","for i, rect in enumerate(ax_wlb.patches):\n","    if i >= len(work_life_balance_attrition_counts):\n","        height = rect.get_height()\n","        if height > 0:\n","            percentage = work_life_balance_attrition_percent['Yes'].iloc[i % len(work_life_balance_attrition_counts)]\n","            x = rect.get_x() + rect.get_width() / 2\n","            y = rect.get_y() + height / 2\n","            ax_wlb.text(x, y, f'{percentage:.1f}%', ha='center', va='center', color='white')\n","\n","plt.tight_layout()\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"5F4679664BB246B29545D208B5EF31F6","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","runtime":{"execution_status":null,"is_visible":false,"status":"default"},"scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[]},"source":["## 薪酬与离职率关系"]},{"cell_type":"code","execution_count":21,"metadata":{"collapsed":false,"id":"CD2A7D45CF244B4795EE9E9A6FA1B498","jupyter":{},"notebookId":"65985827b96e5fc9ebe96f83","scrolled":false,"slideshow":{"slide_type":"slide"},"tags":[],"trusted":true},"outputs":[{"data":{"image/png":"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size 2000x1600 with 4 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(20, 16))\n","# 加薪百分比与离职的关系\n","plt.subplot(2, 2, 1)\n","sns.boxplot(x='Attrition', y='PercentSalaryHike', data=data)\n","plt.title('Attrition by Percent Salary Hike')\n","plt.xlabel('Attrition')\n","plt.ylabel('Percent Salary Hike')\n","\n","# 绩效评级与离职的关系\n","plt.subplot(2, 2, 2)\n","sns.countplot(x='PerformanceRating', hue='Attrition', data=data)\n","plt.title('Attrition by Performance Rating')\n","plt.xlabel('Performance Rating')\n","plt.ylabel('Count')\n","\n","# 股票期权级别与离职的关系\n","plt.subplot(2, 2, 3)\n","sns.countplot(x='StockOptionLevel', hue='Attrition', data=data)\n","plt.title('Attrition by Stock Option Level')\n","plt.xlabel('Stock Option Level')\n","plt.ylabel('Count')\n","\n","# 去年培训时间与离职的关系\n","plt.subplot(2, 2, 4)\n","sns.countplot(x='TrainingTimesLastYear', hue='Attrition', data=data)\n","plt.title('Attrition by Training Times Last Year')\n","plt.xlabel('Training Times Last Year')\n","plt.ylabel('Count')\n","\n","plt.tight_layout()\n","plt.show()"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.7"}},"nbformat":4,"nbformat_minor":0}
